Natural Language Processing and Capstone Assignment

所在平台: Coursera

课程主页: https://www.coursera.org/learn/natural-language-processing-captsone-assignment

课程评论：没有评论

第一个写评论        关注课程

课程简介

课程名称：自然语言处理与顶点项目 课程概述：欢迎参加自然语言处理与顶点项目课程。在本课程中，我们将认识到技术与商业技巧如何结合，以提供商业洞察、竞争情报和消费者情绪的理解。课程最后将通过一个顶点项目，让您应用在本专业中所学到的广泛知识。 课程大纲： 1. 自然语言处理 I： - 介绍文本分析或自然语言处理（NLP）。探索NLP的众多应用，重点讨论其最流行的应用之一——情感分析。 2. 自然语言处理 II： - 继续探索自然语言处理，回顾主题建模以及目前使用的最有效主题检测技术——潜在狄利克雷分配（LDA）。定义文本挖掘中常用的多个技术术语和概念。 3. 数据科学的过去、现在与未来 I： - 提供数据分析相关术语的历史视角，并讨论数据科学中出现的几个关键趋势。探索数据科学的前沿技术，包括深度学习、可解释的人工智能和自动化机器学习。 4. 数据科学的过去、现在与未来 II： - 继续探索数据科学和预测建模的新实践，包括模型集成、传感器技术与物联网、地理空间分析和云计算。通过一项活动，整合在本项目中学到的所有知识，以制定数据分析计划。

课程大纲

Name:Natural Language Processing I

Description:Welcome to Module 1, Natural Language Processing I. In this module we will begin with an introduction to text analytics, or natural language processing (NLP). We will explore the numerous applications of NLP and discuss one of the most popular applications - sentiment analysis.

Name:Natural Language Processing II

Description:Welcome to Module 2, Natural Language Processing II. In this module we will continue our exploration of natural language processing with a review of topic modeling and one of the most effective topic detection techniques currently in use - Latent Dirichlet allocation (LDA). In addition, we will define several technical terms and concepts commonly used in text mining.

Name:The Past, Present, and Future of Data Science I

Description:Welcome to Module 3, Past, Present, and Future of Data Science I. In this module we will provide a historical perspective of the terminology applied to data analytics, as well as a forward-looking discussion of several key trends emerging in data science. We will also explore several leading-edge enablers and enhancers of data science, including deep learning, explainable AI, and automated machine learning.

Name:The Past, Present, and Future of Data Science II

Description:Welcome to Module 4, Past, Present, and Future of Data Science II. In this module we will continue our exploration of new practices in data science and predictive modelling, including model ensembles, sensor technologies and IoT, geospatial analytics, and cloud computing. We will conclude this program with an activity to bring everything you’ve learned in this program together to develop a data analytics plan.

课程评论(0条)